LITERATURE REVIEW

Fixture design has evolved significantly over the last decades, transitioning from heavy, rigid conventional systems to smart, lightweight, AI-optimized solutions. Early research focused on flexible and modular fixtures, which improved setup time and adaptability. Studies by Ivanov, Zajac, and Chan showed that modularity enhances reconfigurability but still results in weight and material inefficiencies.

With advancements in CAD/CAM integration, researchers like Balaykin developed parametric virtual models that allowed simulation-driven validation of fixture geometry. CAFD systems further linked process planning with fixture layout generation.

Recent literature shifts towards AI-powered, optimization-based design approaches.

• Topology optimization redistributes material based on stress flow, producing lighter yet stiffer structures.

• Generative design and cyber-physical systems have introduced automated geometry evolution, producing forms not achievable via traditional design.

• Large Language Models (LLMs) and AI heuristics can propose design modifications, improving load path efficiency and manufacturability.

• ANN-based predictive models have been widely adopted to predict deformation, stress, and stiffness using FEA training data, drastically reducing computational time.

• In additive manufacturing research, studies such as those by Grazia Violante demonstrated the feasibility of producing lattice-based, free-form, high-performance fixtures.

Taken together, the literature indicates a strong industrial shift toward intelligent, lightweight, adaptive, and digitally optimized fixture systems, forming the foundation for this project.